The field of the invention relates generally to maintenance operations, and more specifically, to methods and systems for providing unanticipated demand predictions relating to the maintenance of platforms, such as a flight platform.
A substantial number of airframes built over the past five decades are still in service. Many of these airframes have exceeded their original designed life and have caused an unanticipated demand for replacement and spare parts. Many of these replacement and spare parts demands have been caused by the lengthening of the service life of the aircraft and are for parts that were not originally contemplated as being replaceable. Such parts are referred to herein as low mortality, long lead-time, replacement parts.
Occasionally, replacement parts come from existing platforms that are no longer in service. This process is not typically documented and the scarcity of the available platforms and parts to be cannibalized is necessitating procurement alternatives through traditional sourcing.
The traditional sourcing of low mortality, long lead-time, replacement parts for a platform, such as a flight platform, is difficult and time consuming. Predicting the failure of such parts is difficult, in one aspect, because their host platforms are outlasting their anticipated lifetimes. Therefore, when a part for a platform does fail, or it is determined to replace such a part, it can often take months to procure and then replace the part. During this replacement part lead time, the platform is out of service because a demand for such a part was unanticipated.
It is believed that there are no current solutions that adequately address the need for better prediction of long lead time part failure. Specifically, the majority of the forecasting software that is available does not automatically provide and apply a suggested lifetime model. This forecasting software also does not automatically run goodness of fit and lack of fit tests and make the entire process fluid to a user.
Data collection and mining for field and depot maintenance is time consuming and inconsistent from platform to platform. Other disadvantages are that it takes a well-trained statistician to understand and use the modeling techniques. Currently utilized data collection and mining applications also require that tests for each lifetime model are manually run. Additionally, such applications also dictate that goodness of fit tests and lack of fit tests be run manually.
In order to avoid these long lead times, better failure predictive methods need to be formulated from the known and simulated maintenance data for these types of parts. Field and depot maintenance replacement data is one source of data for such predictive methods.